performance-table-assembly
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/performance-table-assemblyAssembles unified performance tables with confidence intervals from multiple sources
- Solves the problem of inconsistent method and metric reporting across research papers
- Uses extracted scores and condition metadata from prior processing steps
- Aligns metrics, normalizes method names, and validates experimental conditions
- Produces structured JSON output with comparison tables and exclusion rationales
SKILL.md
.github/skills/performance-table-assemblyView on GitHub ↗
---
name: performance-table-assembly
description: Assemble unified comparison table with confidence interval annotations
execution: subagent
prompt: ./prompt.md
input: extracted_scores, condition_metadata
used-by: baseline-establishment
---
# Performance Table Assembly
## Purpose
Combine extracted scores from multiple papers into a single unified comparison table. Handles method name normalization, metric alignment, confidence interval propagation, and fair comparison annotations.
## Input Schema
| Field | Type | Description |
|-------|------|-------------|
| extracted_scores | object[] | Array of score tuples from score-extraction SOP |
| condition_metadata | object[] | Array of condition records from condition-cataloging SOP |
## Output Schema
```json
{
"tables": [
{
"dataset": "string",
"metric": "string",
"higher_is_better": true,
"rows": [
{
"method": "string",
"score": 0.0,
"confidence_interval": [0.0, 0.0],
"num_runs": null,
"conditions_comparable": true,
"condition_notes": "string",
"source": "string",
"year": 2024
}
],
"sota_method": "string",
"sota_score": 0.0,
"fairness_notes": ["string"]
}
],
"excluded_entries": [
{
"method": "string",
"reason": "string"
}
]
}
```